app | ||
src/GEval | ||
test | ||
.gitignore | ||
geval.cabal | ||
LICENSE | ||
NOTICE | ||
README.md | ||
Setup.hs | ||
stack.yaml |
GEval
GEval is a library (and a stand-alone tool) for evaluating the results of solutions to machine learning challenges as defined on the Gonito platform.
Note that GEval is only about machine learning evaluation. No actual machine learning algorithms are available here.
Installing
You need Haskell Stack, then install GEval with:
git clone https://github.com/filipg/geval
cd geval
stack setup
stack install
By default, geval
library is installed in $HOME/.local/bin
, so in
order to run geval
you need to either add $HOME/.local/bin
to
$PATH
or to type:
PATH="$HOME/.local/bin" geval ...
Preparing a Gonito challenge
Directory structure of a Gonito challenge
A definition of a Gonito challenge should be put in a separate directory. Such a directory should have the following structure:
README.md
— description of a challenge in Markdownconfig.txt
— simple configuration file with options the same as the ones accepted bygeval
binary (see below), usually just a metric is specified here (e.g.--metric BLEU
), also non-default file names could be given here (e.g.--test-name test-B
for a non-standard test subdirectory)train/
— subdirectory with training data (if training data are supplied for a given Gonito challenge at all)train/train.tsv
— the usual name of the training data file (this name is not required and could be more than one file), the first column is the target (predicted) value, the other columns represent features, no header is assumeddev-0/
— subdirectory with a development set (a sample test set, which won't be used for the final evaluation)dev-0/in.tsv
— input data (the same format astrain/train.tsv
, but without the first column)dev-0/expected.tsv
— values to be guessed (note thatpaste dev-0/expected.tsv dev-0/in.tsv
should give the same format astrain/train.tsv
)dev-1/
,dev-2
, ... — other dev sets (if supplied)test-A/
— subdirectory with the test settest-A/in.tsv
— test input (the same format asdev-0/in.tsv
)test-A/expected.tsv
— values to be guessed (the same format asdev-0/expected.tsv
), note that this file should be “hidden” by the organisers of a Gonito challenge, see notes on the structure of commits belowtest-B
,test-C
, ... — other alternative test sets (if supplied)
Initiating a Gonito challenge with geval
You can use geval
to initiate a Gonito challenge:
geval --init --expected-directory my-challenge
(This will generate a sample toy challenge about guessing planet masses).
A metric (other than the default RMSE
— root-mean-square error) can
be given to generate another type of toy challenge:
geval --init --expected-directory my-mt-challenge --metric BLEU
Preparing a Git repository
Gonito platform expects a Git repository with a challenge to be submitted. The suggested way to do this is as follows:
- Prepare a branch with all the files without
test-A/expected.tsv
. This branch will be cloned by people taking up the challenge. - Prepare a separate branch (or even a repo) with
test-A/expected.tsv
added. This branch should be accessible by Gonito platform, but should be kept “hidden” for regular users (or at least they should be kindly asked not to peek there). It is recommended (though not obligatory) that this branch contain all the source codes and data used to generate the train/dev/test sets. (Use git-annex if you have really big files there.)
Branch (1) should be the parent of the branch (2), for instance, the repo (for the toy “planets” challenge) could be created as follows:
geval --init --expected-directory planets
cd planets
git init
git add .gitignore config.txt README.md train/train.tsv dev-0/{in,expected}.tsv test-A/in.tsv
git commit -m 'init challenge'
git remote add origin git@github.com:filipg/planets
git push origin master
git add test-A/expected.tsv
git commit -m 'with expected results'
git push origin dont-peek-here
Taking up a Gonito challenge
Clone the repo with a challenge, as given on the Gonito web-site, e.g.
for the toy “planets” challenge (as generated with geval --init
):
git clone https://github.com/filipg/planets
Now use the train data and whatever machine learning tools you like to guess the values for the dev set and the test set, put them, respectively, as:
dev-0/out.tsv
test-A/out.tsv
(These files must have exactly the same number of lines as,
respectively, dev-0/in.tsv
and test-0/in.tsv
. They should contain
only the predicted values.)
Check the result for the dev set with geval
:
geval --test-name dev-0
(the current directory is assumed for --out-directory
and --expected-directory
).
If you'd like and if you have access to the test set results, you can “cheat” and check the results for the test set:
cd ..
git clone https://github.com/filipg/planets planets-secret --branch dont-peek-here
cd planets
geval --expected-directory ../planets-secret
Uploading your results to Gonito platform
Uploading is via Git — commit your “out” files and push the commit to your own repo. On Gonito you are encouraged to share your code, so be nice and commit also your source codes.
git remote add mine git@github.com:johnsmith/planets-johnsmith
git add {dev-0,test-A}/out.tsv
git add Makefile magic-bullet.py ... # whatever scripts/source codes you have
git commit -m 'my solution to the challenge'
git push mine master
Then let Gonito pull them and evaluate your results.